Run
10318445

Run 10318445

Task 31 (Supervised Classification) credit-g Uploaded 18-08-2019 by Rômulo Rodrigues
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sk learn.tree.tree.DecisionTreeClassifier)(4)Automatically created scikit-learn flow.
sklearn.tree.tree.DecisionTreeClassifier(42)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(42)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(42)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(42)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(42)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(42)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(42)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(42)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(42)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(42)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(42)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(42)_random_state1884
sklearn.tree.tree.DecisionTreeClassifier(42)_splitter"best"
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(4)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.impute.SimpleImputer(12)_copytrue
sklearn.impute.SimpleImputer(12)_fill_valuenull
sklearn.impute.SimpleImputer(12)_missing_valuesNaN
sklearn.impute.SimpleImputer(12)_strategy"mean"
sklearn.impute.SimpleImputer(12)_verbose0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

17 Evaluation measures

Area under ROC curve
0.6362 ± 0.0413
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6848 ± 0.0321
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2634 ± 0.0776
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1829 ± 0.0834
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.32 ± 0.0327
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6914 ± 0.0335
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.68 ± 0.0327
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Recall
0.68 ± 0.0327
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7616 ± 0.0777
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5657 ± 0.0289
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2344 ± 0.063
Cross-validation details (10-fold Crossvalidation)